Mining Transportation Information From Social Media for Planned and Unplanned Events

Details

• Creators:
  Zhang, Zhenhua ; Ni, Ming ; Gao, Jing ; He, Qing
• Corporate Creators:
  University at Buffalo, SUNY
• Corporate Contributors:
  University Transportation Centers Program (U.S.) ; United States. Department of Transportation
• Subject/TRT Terms:
  • Social media
  • Data mining
  • Ridership
  • Traffic flow
  • Subways
  • Information retrieval
  • Social media
  • Event identification
  • Subway passenger flow prediction
  • Social sensing
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  New York ; United States
• Corporate Publisher:
  University Transportation Centers Program (U.S.)
• Abstract:
  The objective of this project is on mining social media data to deduce useful traveler’s information with a special emphasis under events, including both planned events (such as sporting games), and unplanned events (such as traffic accidents). Specifically, the project proposes to develop effective and efficient techniques to collect, extract and mine social media data to support advanced traveler information systems and traffic operators. By mining social media based semantics, especially text semantics, this project aims to achieve the following aims: 1) Forecast transit ridership under large sporting games; 2) Identify causality between abnormal traffic flow pattern and social media data; 2) Detect traffic accident using online social media data and traffic loop-detector data.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC48
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/30838/dot_30838_DS1.pdf
• File Type:
  [PDF - 1.15 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:5e6121be99ae4fa67625bcfeb7a540d9cf930981e131615227d1085fb95fe4e3